We present our results in Table REF . The model is trained on 1% of the Atari DQN-replay dataset [1]} (500K transitions for each game). We use the same 4 games used in [2]}: Pong, Seaquest, Qbert, and Breakout. We can see that the proposed model outperforms decision Transformer in all the considered games thus showing the effectiveness of the proposed model. More details regarding the model and training can be found in the appendix section REF .
